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  1. 1
    A harmony search algorithm for university course timetabli

    A harmony search algorithm for university course timetabli by Al-Betar, Mohammed Azmi, Khader, Ahamad Tajudin

    Published 2012
    “…In this paper, a harmony search and a modified harmony search algorithm are applied to university course timetabling against standard benchmarks. …”
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  2. 2
    Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid

    Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid by Wahid, Juliana

    Published 2017
    “…Harmony search algorithm (HSA) is a population-based metaheuristic optimization algorithm that imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. …”
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  3. 3
    Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid

    Hybridizing harmony search with local search based metaheuristic for solving curriculum based university course timetabling / Juliana Wahid by Wahid, Juliana

    Published 2017
    “…Harmony search algorithm (HSA) is a population-based metaheuristic optimization algorithm that imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. …”
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  4. 4
    Adapting And Hybridising Harmony Search With Metaheuristic Components For University Course Timetabling

    Adapting And Hybridising Harmony Search With Metaheuristic Components For University Course Timetabling by Al-Betar, Mohammed Azmi

    Published 2010
    “…Three hybridized versions are proposed which are incremental improvements over the preceding version: (i) Modified Harmony Search Algorithm (MHSA); (ii) Harmony Search Algorithm with Multi-Pitch Adjusting Rate (HSA-MPAR), and (iii) Hybrid Harmony Search Algorithm (HHSA). …”
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  5. 5
    Gravitational search – bat algorithm for solving single and bi-objective of non-linear functions

    Gravitational search – bat algorithm for solving single and bi-objective of non-linear functions by Abbas, Iraq Tareq

    Published 2018
    “…The lack of local optimum mechanism decreases the intensification of the search space, whereas diversity remains high. Secondly, two meta-heuristics, namely, Bi-Objective Gravitational Search Algorithm (BOGSA) and Bi-Objective Bat Algorithm (BOBAT), were combined to form a (BOGS-BAT) algorithm. …”
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  6. 6
    A Micro-Genetic Algorithm Approach for Soft Constraint Satisfaction Problem in University Course Scheduling

    A Micro-Genetic Algorithm Approach for Soft Constraint Satisfaction Problem in University Course Scheduling by Abd. Halim, Bohadean @ Bohari

    Published 2013
    “…A university course timetabling problem is a combination of optimization problems. …”
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  7. 7
    Harmony great deluge for solving curriculum based course timetabling problem

    Harmony great deluge for solving curriculum based course timetabling problem by Wahid, Juliana, Mohd Hussin, Naimah

    Published 2013
    “…University course timetabling which has been determined as non deterministic polynomial problem that accept widely as problem that are intractable.An efficient algorithm does not exist that is guaranteed to find an optimal solution for such problems.The design of good algorithm to find new methods and techniques to solve such problem is a very active area of research.This paper presents the adaption of the hybridizing between harmony search with great deluge algorithm for solving curriculum-based course timetabling problems.The algorithm can be adapted to the problem.Results were not comparatively better than those previously known as best solution.Proper modification in terms of the approach in this algorithm would make the algorithm perform better on curriculum-based course timetabling.…”
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  8. 8
    Meta-Heuristic Algorithms for Learning Path Recommender at MOOC

    Meta-Heuristic Algorithms for Learning Path Recommender at MOOC by Son, N.T., Jaafar, J., Aziz, I.A., Anh, B.N.

    Published 2021
    “…We have developed Metaheuristic algorithms includes the Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), to solve the proposed model. …”
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  9. 9
    Optimization of blood vessel detection in retina images using multithreading and native code for portable devices

    Optimization of blood vessel detection in retina images using multithreading and native code for portable devices by Tran, Duc Ngoc, Hussin, Fawnizu Azmadi, Yusoff, Mohd Zuki

    Published 2013
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  10. 10
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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  11. 11
    A Genetic Algorithm for the Real-world University Course Timetabling Problem

    A Genetic Algorithm for the Real-world University Course Timetabling Problem by Chee, Hung Wong, Goh, Say Leng, Jonathan Likoh Juis @ Juise

    Published 2022
    “…Experiments are conducted to set the best parameter values for the algorithm to operate optimally under a computation time limit. …”
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  12. 12
    Route Optimization System

    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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  13. 13
    Shortest Path Routing Using Heuristic Search

    Shortest Path Routing Using Heuristic Search by Alaiwan, Ahmed Omran A.

    Published 2006
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  14. 14
    Malaysian menu planning model using Self-adaptive Hybrid Genetic Algorithm (SHGA)

    Malaysian menu planning model using Self-adaptive Hybrid Genetic Algorithm (SHGA) by Mohd Razali, Siti Noor Asyikin, Engku Abu Bakar, Engku Muhammad Nazri, Ku-Mahamud, Ku Ruhana, Arbin, Norazman, Rusiman, Mohd Saifullah

    Published 2018
    “…Two new novel local searches are introduced in this study that combined the insertion search (IS) and insertion search with delete-and-create (ISDC) methods. …”
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  15. 15
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
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  16. 16
    Hybrid Metaheuristic Algorithm and Metaheuristic Performance Measurement for Solving University Course Timetabling Problem

    Hybrid Metaheuristic Algorithm and Metaheuristic Performance Measurement for Solving University Course Timetabling Problem by Md Sultan, Abu Bakar

    Published 2007
    “…Metaheuristics have received considerable interest in the fields of applied artificial intelligence and combinatorial optimization such as university course timetabling problem (UCTP). …”
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  17. 17
    Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA)

    Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA) by Mohd Razali, Siti Noor Asyikin, Engku Abu Bakar, Engku Muhammad Nazri, Ku Mahamud, Ku Ruhana, Arbin, Norazman, Rusiman, Mohd Saifullah

    Published 2018
    “…Two new novel local searches are introduced in this study that combined the insertion search (IS) and insertion search with deleteand-create (ISDC) methods. …”
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  18. 18
    Design and statistical analysis of initial solution construction approach in curriculum based course timetabling problem

    Design and statistical analysis of initial solution construction approach in curriculum based course timetabling problem by Wahid, Juliana, Mohd Hussin, Naimah

    Published 2017
    “…This process is a crucial task because it can affect the convergence speed and also the quality of the final solution (Rahnamayan, Tizhoosh, & Salama, 2007).This study able to produce a set of initial solution, therefore it is able to contribute to the improvement phase of approach that uses population of initial solutions such as ant colony optimization (ACO) (Socha, Joshua, & Michael, 2002), genetic algorithm (GA) (Lewis & Paechter, 2005), and harmony search algorithm (HSA) (Al-Betar & Khader, 2010).The approach in this study also shows that a feasible timetable can be found for numerous data set problems.…”
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  19. 19
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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  20. 20
    A PSO inspired asynchronous cooperative distributed hyper-heuristic for course timetabling problems

    A PSO inspired asynchronous cooperative distributed hyper-heuristic for course timetabling problems by Joe Henry Obit, Rayner Alfred, Mansour Hassani Abdalla

    Published 2017
    “…The proposed hyper-heuristic algorithm starts with a complete solution and tries to improve the soft constraints, whilst always remaining in the feasible region of the search space. …”
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